OntheCausalitybetweenDomesticCreditAggregatesandEconomicGrowthinaMultivariateVARFramework:EvidencefromNigeria Evans,Olaniyi MunichPersonalRePEcArchive

(1)

On the Causality between Domestic

Credit Aggregates and Economic Growth in a Multivariate VAR Framework:

Evidence from Nigeria

Evans, Olaniyi

Department of Economics, University of Lagos, Nigeria

November 2013

Online at https://mpra.ub.uni-muenchen.de/51731/

MPRA Paper No. 51731, posted 29 Nov 2013 05:29 UTC

(2)

On the Causality between Domestic Credit

Aggregates and Economic Growth in a Multivariate VAR Framework: Evidence from Nigeria

Abstract

The major objective of this paper is to empirically investigate the relationship between domestic credit and economic growth in Nigeria, using annual time series data from 1970 to 2012. In order to do this, the study employs KPSS unit root test, Johansen cointegration test, VAR modeling, impulse response function, variance decomposition and granger causality. Firstly, the findings reveal that there is a bi-directional causality and positive relationship between domestic credit and the economic growth in Nigeria. That is, domestic credit does not only contribute positively to economic growth in Nigeria, but the impact is strong and statistically significant. The findings have a strong implication on financial policy in Nigeria. The major implication is that an efficient financial system is one of the foundations for building sustained economic growth.

Considering regulations, institutional constraints and other macro-economic factors militating against domestic credit in the economy, government should make the environment conducive and supportive so that performance is enhanced and good lending behaviour guaranteed.

(3)

1. Introduction

Mirdala (2011), Baltagi (2008), Abu-Bader and Abu-Qarn (2008), Demetriades and Andrianova (2004) and Godhart (2004) contend that a sound financial system is very essential and prime requirement for economic growth. Schumpeter (1911), Gurley and Shaw (1955), Goldsmith (1969) and Hicks (1969), all notable early works on finance and growth, also show that finance is crucially important in stimulating economic growth. Together with financial liberalization and international financial integration, financial intermediation was encouraged especially due to its potential effects on the real economy. Thus, the size of the financial sector is usually closely related to the overall economic performance of the country.

An efficient financial system is one of the foundations for building sustained economic growth and an open, vibrant economic system. In the early neoclassical growth literature, financial services played a role of channeling household savings to investors. Levine (2005) suggests that financial institutions and markets can foster economic growth through allocating savings to their most productive use. The finance-led growth hypothesis postulates the supply-leading relationship between financial and economic developments (Patrick, 1966). According to this view, the existence of a financial sector, as well as well-functioning financial intermediations that channel the limited resources from surplus units to deficit units, would provide efficient allocation resources, thereby leading other economic sectors in their growth process. This view has received considerable support from recent empirical studies (Habibullah and Eng, 2006) According to CBN (2003), the amount of loans and advances given by the banking sector to economic agents constitute bank credit. Bank credit is often accompanied with some collateral that helps to ensure the repayment of the loan in the event of default. Credit channels savings into productive investment thereby encouraging economic growth. Thus, the availability of credit allows the role of intermediation to be carried out, which is important for the growth of the economy.

The broad consensus that credit from banks and other financial institutions play an important role in generating growth and reducing poverty is in no doubt. This is because availability of credit augments the purchasing power of individuals and households, and this has a multiplier effect on

(4)

the economy of any nation. Nigeria is a big economy and it poses as a very good window for investors to get started on the continent, which will benefit the whole of sub-Saharan Africa.

Nigeria is gradually developing a full-blown consumer credit market. An efficient and functioning market for credit will stimulate business activity and support Nigeria‘s dynamic entrepreneurs. There is a prevalent feeling that following the banking sector crisis which erupted in August 2009, this process has stalled as commercial banks have curtailed their lending to the private sector causing a credit crunch or squeeze. However, the syndication by FirstBank, Zenith Bank, Access Bank, Fidelity Bank, United Bank for Africa, Bank PHB, Guaranty Trust Bank and Oceanic Bank was a major departure from that lull in lending that brought untold hardship to manufacturers. The $650 million loan to Etisalat in 2011 was the biggest loan so far syndicated by Nigerian banks after the global financial crisis which slowed lending activities drastically (Businessday, 2011).

Considering recent reforms and positive growth in macro-indices, Nigeria has a massive opportunity to lay the foundations for a strong financial sector. At an annual average growth rate above 7 percent over the past few years, the Nigerian economy is one of the fastest growing economies in the world. While the government is ambitious to make the country one of the world’s 20 largest economies by 2020, a weak financial sector, particularly with respect to credit services to individuals and households at the bottom of the pyramid, has remained an impediment.

Still smarting from the crippling impact of the global economic crisis, followed by the banking sector reform by the Central Bank of Nigeria (CBN) termed as ‘The Sanusi Tsunami’, banks have become lending-shy and a lot more cautious with the kind of projects they finance. For example, real estate, with its long gestation period is not always in the immediate loan consideration of most banks. The low level of supply of domestic credit aggregates in general and money stock in particular had been responsible for the fundamental failure of many African countries to attain growth and development. Various scholars have laid much of the blame for the failure of monetary policies to translate into economic growth on the government and its agencies as a result of poor implementation and insincerity on the part of policy executors (Ojo, 1993).

(5)

It is widely accepted that the private sector is the engine of growth. The creation of an enabling environment will make it achievable. One of the major challenges identified as constraints to doing business in Nigeria is lack of access to finance as well as the cost of finance. Providing finance to Nigeria‘s real sector especially the small and medium enterprises (SMEs) will be a key component of future job creation. Nigeria‘s banks have never successfully lent to the nation‘s SMEs. There is a consensus within the banking sector regarding the constraints on SME access to credit. These constraints fall into three broad categories: (a) infrastructure constraints, (b) supply-side constraints, which relate to the incentives for banks to lend to SMEs; and (c) environmental constraints, which reflect the combination of institutional, legal, infrastructure, and capacity problems which make SME lending cumbersome and risky (Radwan, 2010). Yet, the apathy of some banks towards small savers in the country as well as the declining credit to small businesses have been largely attributed to the amount of returns the financial institutions get from playing with public sector funds.

As a matter of fact, most banks in Nigeria have historically tended to concentrate lending to the corporate and commercial segments of the market, thereby locking-out the retail/consumer segment from the credit system; largely on account of the lack of credit information on individuals and persons in the country, which make up that segment. The sectors that are driving GDP growth have little exposure to credit. About 80 percent of Nigeria’s private credit goes to sectors of the economy that account for only 23 percent of real GDP growth, according to data from the CBN and NBS. Agriculture, for example, is responsible for almost 30 percent of real GDP growth; however, only 2 percent of credit extended goes to the agriculture sector. Two other important drivers of growth, trade and communications (and transport), which are responsible for 26 percent and 22 percent of growth respectively, also received relatively low shares of private credit – 11 percent and 10 percent, respectively.

Not until recently, with the recapitalization in the banking sector which resulted in mergers, acquisitions increased bank branches and innovations, the Nigerian financial system remained by and large relatively underdeveloped because of dearth of financial intermediation and financial deepening which the economy requires for sustained growth. Since the financial system performs the vital function of raising funds, and channelling funds to productive investment, successful

(6)

domestic credit is usually an important component of a country’s strategy for economic growth.

As well, considering the various financial restructuring programs in ensuring that private sector takes its place as the engine of economic growth, one still wonders if the domestic credit has actually made any significant impact. Therefore, studying the effect of domestic credit on economic growth is a vital one, considering the continuing progress in Nigeria’s financial sector, especially at the aftermath of the global financial crisis. This study, therefore, combines an analysis of a set of domestic credit aggregates for Nigeria and an empirical investigation into the domestic credit–economic growth link.

2. Literature Review

This section gives an empirical review of domestic credit and its effect on economic growth. It gives a detailed explanation of various works being embarked upon by researchers in this field and a review that can be sited within the Nigerian context.

Muhsin and Eric (2000) find, in a study of Turkish economy, that when bank deposit, private sector credit or domestic credit ratios are alternatively used as proxies for financial development;

causality runs from economic growth to financial development. They conclude that growth leads financial sector development.

Wa (2005) find, in a study of bank credit and economic growth in Macao, that the elasticity of output with respect to bank credit has fluctuated over time and exhibited a downward trend. As well, the contribution of domestic bank credit has been less significant in this growth process, as large-scale tourism projects have been largely financed by foreign funds.

Dey and Flaherty (2005), using a two-stage regression model, examines the impact of bank credit and stock market liquidity on GDP growth. They find that bank credit and stock market liquidity are not consistent determinants of GDP growth. On the contrary, banking development is a significant determinant of GDP growth.

(7)

Mishra et al (2009), using VAR, investigates the direction of causality between India credit market development and the economic growth between 1980 and 2008. He finds that economic growth has a positive impact on credit market development. As well, Granger Causality Tests indicate that credit market development spurs economic growth in India.

Cappiello et al (2010) find, in their study of the European Area, that contrary to recent findings for the US, the supply of credit, both in terms of volumes and in terms of credit standards applied on loans to enterprises, have significant impacts on real economic activity. That is to say, a change in loans growth has a positive significant impact on GDP.

Akpansung and Babalola ( 2011) examines the relationship between banking sector credit and economic growth in Nigeria over the period 1970-2008. They establish the causal links between the variable, using Granger causality test while using a Two-Stage Least Squares (TSLS) estimation technique for their regression models. The results of Granger causality test show evidence of unidirectional causal relationship from GDP to private sector credit (PSC) and from industrial production index (IND) to GDP. They find that private sector credit impacts positively on economic growth while lending rate impedes economic growth.

Iqbal et al (2012), using ARDL approach and error correction model (ECM), find that the national savings and credit to private sector plays important role in economic growth and development of Pakistan. The results indicate that increase in real gross domestic product was 5.59 percent due to one percent increase in credit to private sector. In this way, the credit to private sector has significant impact upon economic growth in the long run but also in the short run.

By and large, this review of related studies suggests that the causal relation between domestic credit and economic growth is still controversial in the literature. Apart from being not ample, the empirical literature is enfeebled by not covering the period after the recent global financial crisis. This paper endeavours to fill such gaps.

(8)

3. Theoretical Framework

Harrod-Domar growth model is about the conditions for stable growth in an economy. It assumes that aggregate demand and supply would be in balance when investment (It) in any period equals the change in national income (Yt -Yt-1) times the capital to output ratio (k). The capital to output ratio indicates the value of capital required to produce one unit of output in a single time period.

At equilibrium in a closed economy, intended investment would equal intended savings (St), which gives the initial equilibrium condition.

It = St = k(Yt -Yt-1) (1)

Divided by Yt

⁽⁾ ⁽²⁾

Define:

Savings rate (3)

⁽⁾ Growth rate (4)

Focus:

s = k x g or Harrod-Domar growth equation (5)

The rate of growth is determined jointly by the national savings ratio and national capital to output ratio. The more a nation can save and invest the quicker it can grow!

Thus, it appears plausible that one of the underlying reasons that savings is less effective in spurring development than is expected may be due to the failure of the financial system in ensuring its efficient allocation as domestic credit. In other words, savings functions effectively when it is well-transformed into domestic credit in a sound financial system.

(9)

4. Model Specification and Estimation Techniques 4.1 Model Specification

The quest to examine the impact of domestic credit on economic growth gives rise to the model to be adopted. Various indicators of domestic credit abound but to really explain the impact of domestic credit on Nigerian economic growth, there is need to take cognizance of empirical works being carried out by various researchers in the field.

Following a detailed review of previous studies and improving upon the theoretical postulates described above, economic growth is expressed as a function of net domestic credit, CREDIT, and a set of control variables. This is expressed by equation (6) below;

GDPCAPITAL = f { CREDIT,MONEY,INVESTMENT,LENDING, INTEREST } (6)

GDPCAPITAL = Ɋ0 + Ɋ1 CREDIT + Ɋ2 MONEY + Ɋ3 INVESTMENT + Ɋ4 LENDING + Ɋ5 INTEREST + ξ (7)

GDPCAPITAL = GDP per Capital CREDIT = Net Domestic Credit

MONEY= Money and Quasi-Money (M2)

INVESTMENT = Gross Capital Formation as a Percentage of GDP LENDING = Lending Rate

INTEREST = Real Interest Rate

The a priori expectations are: Ɋ1, Ɋ2, Ɋ3 > 0 and Ɋ4 , Ɋ5 < 0.

In using the Multiple Regression Model, the following assumptions are made:

 There is a linear relationship between the dependent variable – GDPCAPITAL and CREDIT, MONEY, INVESTMENT, LENDING and INTEREST. Hence, the functional relationship: GDPCAPITAL = f { CREDIT, MONEY, INVESTMENT, LENDING, INTEREST }.

 Both dependent and independent variables are continuous random variable which is normally distributed.

(10)

 The random terms of different observations (ξⁱ, ξ^j) are independent. This means that all the covariances of any ξⁱ, with any other ξ^j are equal to zero. The value which the random term assumes in one period does not depend on the value which it assumed in any other period.

 The explanatory variables are not perfectly linearly correlated. If there is more than one explanatory variable in the relationship it is assumed that they are not perfectly correlated with each other. Indeed the regressors should not be highly multicollinear.

The data for the empirical analysis are obtained from the World Bank database.

4.2 Estimation Techniques

This study employs KPSS unit root test, Johansen cointegration test, VAR modeling, impulse response function, variance decomposition and granger causality. No other study has gone to such extent to estimate the nexus between domestic credit and economic growth in Nigeria 4.2.1 Stationarity Test

This study uses the stationarity test to test if the given series has unit root. Stationarity of a series is an important phenomenon because it can influence its behaviour. If x and y series are non- stationary random processes (integrated), then modelling the x and y relationship as a simple OLS relationship as in the following equation will only generate a spurious regression.

Y = α + X + ξt

Time series stationarity is the statistical characteristics of a series such as its mean and variance over time. If both are constant over time, then the series is said to be a stationary process (i.e. is not a random walk/has no unit root), otherwise, the series is described as being a non-stationary process (i.e. a random walk/has unit root). Differencing a series using differencing operations produces other sets of observations such as the first-differenced values, the second-differenced values and so on.

x level xt

x 1^st -diferenced value xt – xt-1

x 2^nd -diferenced value xt – xt-1

If a series is stationary without any differencing it is designated as I (0), or integrated of order 0.

On the other hand, a series that has stationary first differences is designated I (1), or integrated of order one (1). KPSS test will be used to test the stationarity of the variables.

(11)

4.2.2 Johansen and Juselius Cointegration Test

This study uses two tests to determine the number of cointegration vectors: the Maximum Eigenvalue test and the Trace test. The Maximum Eigenvalue statistic tests the null hypothesis of r cointegrating relations against the alternative of r+1 cointegrating relations for r = 0, 1, 2…n-1.

This test statistics are computed as:

LRmax(r/n+1) = -T*log(1-λ) (8)

Where λ is the Maximum Eigenvalue and T is the sample size. Trace statistics investigate the null hypothesis of r cointegrating relations against the alternative of n cointegrating relations, where n is the number of variables in the system for r = 0, 1, 2…n-1. Its equation is computed according to the following formula:

( ) ∑ ( ) (9) In some cases Trace and Maximum Eigenvalue statistics may yield different results. In this case the results of trace test should be preferred.

4.2.3 Vector Autoregression Model (VAR)

This study applies VAR in order to evaluate the short run properties of the model. If cointegration has been detected between series we know that there exists a long-term equilibrium relationship and we use VECM (VAR error correction model). In case of no cointegration, VAR is used. Then, one directly proceeds to Granger causality tests to establish causal links between the variables. The regression equation form for VAR is as follows:

∑ ∑ ∑ (10)

∑ ∑ ∑ (11)

In VAR, the cointegration rank shows the number of cointegrating vectors. For instance a rank of two indicates that two linearly independent combinations of the non-stationary variables will be stationary.

4.2.4 Granger Causality test

Causality is a kind of statistical feedback concept which is widely used in the building of forecasting models. The definition states that in conditional distribution, lagged values of Y add no information to explanation of movements of Xt beyond that provided by lagged values of Xt

(12)

itself (Green, 2003). In summary, one variable (Xt) is said to granger cause another variable (Yt) if the lagged values of Xt can predict Y and vice versa.

In this study, the Granger causality test was performed using the following vector autoregressive (VAR) models:

If causality (or causation) runs from CREDIT to GDPCAPITAL, we have:

∑ ∑

(12)

If causality (or causation) runs from GDP to M2GDP, it takes the form:

∑ ∑

(13)

It is assumed that the disturbance terms ξ1tand ξ2t are uncorrelated.

The decision rule:

From equation (3.8), CREDIT-j Granger causes GDPCAPITAL if the coefficient of the lagged values of CREDIT as a group ( j) is significantly different from zero based on F-test (i.e., statistically significant). Similarly, from equation (3.9), GDPCAPITALt-j Granger causes CREDIT if 𝑗 is statistically significant.

5. Empirical Estimation and Analysis of Model Results

Empirical evidence justifies a belief in the truth or falsity of an empirical claim. This study requires empirical evidence for the hypothesis that domestic credit has significant impact on economic growth in Nigeria to be accepted. Therefore, this section aims at validating the a priori expectations of the variables by determining the causal relationship between the dependent and independent variables employing a series of tests including stationarity test, cointegration test, VAR, impulse response and variance decomposition.

5.1 Ordinary Least Square Estimation of the Model

GDPCAPITAL = 4.173830 - 0.011654*CREDIT + 0.085546*MONEY + 0.228371*INVESTMENT - 0.006030*LENDING - 0.000104*INTEREST

Durbin Watson = 0.598057 R-Squared = 0.717895

(13)

Durbin Watson statistic is much lower than R-Squared, suggesting evidence of spurious regression. We can, as well, re-estimate the equation, adding AR(1).

GDPCAPITAL = 4.207811 - 0.004877*CREDIT + 0.088661*MONEY + 0.048835*INVESTMENT - 0.001191*LENDING + 0.000789*INTEREST + [AR(1)=0.942033932639]

Durbin Watson = 1.464832 R-Squared = 0.920030

Now Durbin Watson statistic is much greater than R-Square. However, the estimate of ρ is very high indeed, 0.94, suggesting that there was very high autocorrelation in the original specification. Yet, this could be as a result of mis-specification of the model. The unsuitability of the application of the ordinary least square on a multiple linear regression model is due to the fact that some of the variables – dependent variables and independent variables – may be non- stationary at levels, thus, suggesting the possibility of spurious regressions.

5.2 Stationarity Test

Since most economic time series are not very informative about whether or not there is a unit root, it would be useful to perform tests of the null hypothesis of stationarity as well as tests of the null hypothesis of a unit root (Kwiatkowski, Phillips, Schmidt and Shin, 1992). Unit root test using the Kwiatkowski-Phillips-Schmidt-Shin test statistic (KPSS) to confirm stationarity is carried out at both 5 percent and 1 percent levels of significance. The choice of the KPSS test is motivated by the argument that tests designed on the basis of the ADF have low predictive power.

Table 1: KPSS Test

Null Hypothesis (KPSS): The Variable is Stationary.

Intercept Trend & Intercept Levels

GDPCAPITAL 0.328429** 0.179396

CREDIT 0.715248 0.087565**

MONEY 0.826420 0.111958**

INVESTMENT 0.654058 0.149624

LENDING 0.545525 0.156612

INTEREST 0.633710 0.110443**

First Difference

(14)

ΔGDPCAPITAL 0.140005** 0.125717**

ΔCREDIT 0.097312** 0.086243**

ΔMONEY 0.107375** 0.104024**

ΔINVESTMENT 0.139461** 0.107168**

ΔLENDING 0.134319** 0.075227**

ΔINTEREST 0.209983** 0.108845**

Critical Values 1% 0.739000 0.216000

5% 0.463000 0.146000

10% 0.347000 0.119000

Notes: An * indicates rejection of the null hypothesis of non-stationarity at the 5 percent level of significance, while ** indicates a stronger rejection at the 1 percent level. Number of lags was selected using the AIC criterion. For KPSS, Barlett-Kernel is used as the spectral estimation method. The bandwidth is selected using Newey-West method.

Using KPSS test indicates that most of the economic variables included in the model are non- stationary at levels both @ intercept and @ trend. Further, all the variables maintain stationarity at an integration of order one, I(1). Hence, higher order of integration is needless.

The above unit root results validate the unsuitability of the application of the ordinary least square on a multiple linear regression model. This is due to the fact that some of the variables – dependent variables and independent variables – are non-stationary at levels, thus, suggesting the possibility of spurious regressions.

5.3 Test of Lag Length Selection

The Schwarz Information Criterion (SC) is used to select the optimal lag length. Based on the SIC, it is found that one lag is optimal. SC is used for model selection such as determining the lag length of a model, with smaller values of the information criterion being preferred.

Table 2: VAR Lag Order Selection Criteria VAR Lag Order Selection Criteria

Endogenous variables: GDPCAPITAL CREDIT MONEY INVESTMENT INTEREST LENDING

Sample: 1970 2012 Included observations: 39

Lag LogL LR FPE AIC SC HQ

0 -419.6607 NA 121.7000 21.82875 22.08469 21.92058 1 -209.9596 344.1250 0.016862 12.92100 14.71253* 13.56379*

(15)

2 -177.6303 43.10572 0.023304 13.10924 16.43637 14.30299 3 -126.1179 52.83321* 0.015430* 12.31374 17.17646 14.05844 4 -74.25459 37.23519 0.016566 11.50024* 17.89855 13.79590 * indicates lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error

AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion 4.4 Cointegration Test

The Johansen co-integration test procedure is used to determine the cointegrating relationships among the variables. This consists of the the Trace criterion and the Maximum Eigenvalue criterion.

Table 3 Multivariate Cointegration Test Results:The Johansen-Juselius Approach Sample (adjusted): 1972 2012

Included observations: 41 after adjustments Trend assumption: Linear deterministic trend

Series: GDPCAPITAL CREDIT MONEY INVESTMENT INTEREST LENDING Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.564737 105.2087 95.75366 0.0095

At most 1 * 0.483352 71.10469 69.81889 0.0394

At most 2 0.457054 44.02853 47.85613 0.1094

At most 3 0.239675 18.98793 29.79707 0.4938

At most 4 0.139871 7.753549 15.49471 0.4921

At most 5 0.037709 1.575958 3.841466 0.2093

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

(16)

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.564737 34.10403 40.07757 0.2017

At most 1 0.483352 27.07616 33.87687 0.2593

At most 2 0.457054 25.04060 27.58434 0.1023

At most 3 0.239675 11.23438 21.13162 0.6239

At most 4 0.139871 6.177590 14.26460 0.5905

At most 5 0.037709 1.575958 3.841466 0.2093

Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Table 3 reveals the presence of no cointegrating vectors from the maximal eigenvalue statistic while the trace test statistic indicates the existence of 2 cointegrating equations at the 5% level.

The maximal eigenvalue statistic forms the basis of the formulation of a one-vector model in order to investigate the direct effect of the domestic credit on real output. The non-existence of Cointegration is indicative of a short-run relationship between real output and the domestic credit variables.

5.5 Results of the VAR Model

Table 4 Vector Autoregression Estimates Vector Autoregression Estimates

Sample (adjusted): 1971 2012

Included observations: 42 after adjustments Standard errors in ( ) & t-statistics in [ ]

GDPCAPIT

AL CREDIT MONEY

INVESTM

ENT INTEREST LENDING

GDPCAPITAL(-1) 1.196229 -15.38124 0.508535 1.078644 5.067536 -4.217118 (0.09154) (6.01717) (0.26605) (0.35777) (24.2558) (5.39058) [ 13.0680] [-2.55622] [ 1.91144] [ 3.01488] [ 0.20892] [-0.78231]

CREDIT(-1) 0.007017 -0.164143 -0.007835 -0.003504 0.519619 0.089717 (0.00257) (0.16915) (0.00748) (0.01006) (0.68186) (0.15154) [ 2.72680] [-0.97039] [-1.04756] [-0.34835] [ 0.76206] [ 0.59205]

MONEY(-1) -0.022459 2.654293 0.961353 -0.076630 1.295500 0.133801 (0.00924) (0.60742) (0.02686) (0.03612) (2.44857) (0.54417)

(17)

[-2.43043] [ 4.36978] [ 35.7952] [-2.12175] [ 0.52908] [ 0.24588]

INVESTMENT(-1) -0.095071 4.563653 -0.106635 0.540213 -5.335534 -1.512563 (0.03167) (2.08185) (0.09205) (0.12378) (8.39215) (1.86506) [-3.00181] [ 2.19211] [-1.15846] [ 4.36414] [-0.63578] [-0.81100]

INTEREST(-1) -0.000695 -0.038719 0.000554 -0.002886 -0.073694 -0.032454 (0.00063) (0.04165) (0.00184) (0.00248) (0.16789) (0.03731) [-1.09668] [-0.92966] [ 0.30091] [-1.16561] [-0.43895] [-0.86981]

LENDING(-1) -0.000858 -0.024160 0.010862 -0.005222 -0.772916 0.719175 (0.00188) (0.12358) (0.00546) (0.00735) (0.49815) (0.11071) [-0.45635] [-0.19550] [ 1.98787] [-0.71066] [-1.55157] [ 6.49611]

C -0.618192 50.93569 -1.815742 -3.784115 -54.76777 30.45441 (0.42722) (28.0824) (1.24166) (1.66974) (113.203) (25.1581) [-1.44703] [ 1.81380] [-1.46235] [-2.26628] [-0.48380] [ 1.21052]

R-squared 0.919413 0.637910 0.997981 0.909949 0.184992 0.844893 Adj. R-squared 0.905598 0.575838 0.997635 0.894512 0.045276 0.818303 Sum sq. resids 0.079391 343.0397 0.670629 1.212765 5574.304 275.3158 S.E. equation 0.047627 3.130676 0.138423 0.186146 12.62006 2.804669 F-statistic 66.55181 10.27686 2883.183 58.94471 1.324058 31.77512 Log likelihood 72.09639 -103.6991 27.28596 14.84468 -162.2487 -99.08065 Akaike AIC -3.099828 5.271387 -0.965998 -0.373556 8.059464 5.051460 Schwarz SC -2.810217 5.560999 -0.676386 -0.083945 8.349075 5.341071 Mean dependent 6.577778 25.00809 25.60468 2.621490 -1.309395 15.30424 S.D. dependent 0.155010 4.806978 2.846203 0.573128 12.91583 6.579729 Determinant resid covariance

(dof adj.) 0.007616

Determinant resid covariance 0.002551

Log likelihood -232.1726

Akaike information criterion 13.05584 Schwarz criterion 14.79351

Since we are considering only the GDPCAPITAL vector among the system of equations, we re- estimate the GDPCAPITAL equation as OLS. This gives the results in table 5.

Table 5: Results of the VAR Model Dependent Variable: GDPCAPITAL

Method: Least Squares

Sample (adjusted): 1971 2012

(18)

Included observations: 42 after adjustments

GDPCAPITAL = C(1)*GDPCAPITAL(-1) + C(2)*CREDIT(-1) + C(3) *MONEY(-1) + C(4)*INVESTMENT(-1) + C(5)*INTEREST(-1) + C(6)*LENDING(-1) + C(7)

Coefficient Std. Error t-Statistic Prob.

C(1) 1.196229 0.091539 13.06798 0.0000

C(2) 0.007017 0.002573 2.726802 0.0099

C(3) 0.022459 0.009241 2.430428 0.0203

C(4) 0.095071 0.031671 3.001810 0.0049

C(5) -0.000695 0.000634 -1.096677 0.2803

C(6) -0.000858 0.001880 -0.456351 0.6510

C(7) -0.618192 0.427216 -1.447025 0.1568

R-squared 0.919413 Mean dependent var 6.577778

Adjusted R-squared 0.905598 S.D. dependent var 0.155010 S.E. of regression 0.047627 Akaike info criterion -3.099828 Sum squared resid 0.079391 Schwarz criterion -2.810217 Log likelihood 72.09639 Hannan-Quinn criter. -2.993674

F-statistic 66.55181 Durbin-Watson stat 1.965401

Prob(F-statistic) 0.000000

As shown in Table 5, at the threshold of 0.05 level of significance, CREDIT, MONEY and INVESTMENT have positive statistically significant impact on GDPCAPITAL. As well, LENDING and INTEREST have negative statistically insignificant impact on GDPCAPITAL.

The coefficient of determination (R²) which gives 0.919413 indicates that the model explains 92 percent of the variations in GDPCAPITAL. This shows a very good fit as only about 8%

variation in GDP is left accounted for by the model. The Durbin-Watson statistic of 1.965401 is within the bounds of non-autocorrelation. The F-statistic is 66.55181. This value is significant at 1%, 5% and 10% because the calculated Prob(F-statistic) = 0.000000. With this, we reject the null hypothesis that all the explanatory variables introduced in the model are not jointly significant in explaining the variations in GDPCAPITAL and conclude that they are simultaneously significant.

5.5 Granger Causality

Granger causality is applied to check for the direction of causation. The results of Pairwise Granger Causality between GDPCAPITAL and CREDIT are contained in Table 6. The results

(19)

reveal the existence of a bi-directional causality which runs from GDPCAPITAL to CREDIT and from CREDIT to GDPCAPITAL.

Table 6: VAR Granger Causality/Block Exogeneity Wald Tests Sample: 1970 2012

Included observations: 42

Dependent variable: GDPCAPITAL

Excluded Chi-sq df Prob.

CREDIT 7.435451 1 0.0064

MONEY 5.906978 1 0.0151

INVESTMENT 9.010861 1 0.0027

INTEREST 1.202700 1 0.2728

LENDING 0.208256 1 0.6481

All 15.24514 5 0.0094

Dependent variable: CREDIT

Excluded Chi-sq df Prob.

GDPCAPITAL 6.534283 1 0.0106

MONEY 19.09500 1 0.0000

INVESTMENT 4.805352 1 0.0284

INTEREST 0.864269 1 0.3525

LENDING 0.038222 1 0.8450

All 33.97186 5 0.0000

The null hypothesis that the variables are not significant in Granger-causing each other is rejected. The alternative hypothesis is accepted that the variables Granger-cause each other.

Our finding of bi-directional causality between GDPCAPITAL and CREDIT can be strengthened by the plots of ‘Impulse Responses’ and ‘Variance Decomposition’ as shown below.

(20)

5.6 Impulse Response Functions

The impulse response describes the reaction of the system as a function of time (or possibly as a function of some other independent variable that parameterizes the dynamic behavior of the system). The impulse response function for the variables is depicted in Table 6.

Table 7: Impulse Response Functions

It can be seen that a positive shock to CREDIT results in positive response of GDPCAPITAL.

Conversely, a negative shock to GDPCAPITAL results in negative response of CREDIT. In fact,

-.04 -.02 .00 .02 .04 .06

1 2 3 4

GDPCAPITAL CREDIT MONEY

INVESTMENT INTEREST LENDING Response of GDPCAPITAL to Cholesky

One S.D. Innovations

-1 0 1 2 3 4

1 2 3 4

INVESTMENT INTEREST LENDING Response of CREDIT to Cholesky

-.10 -.05 .00 .05 .10 .15

1 2 3 4

INVESTMENT INTEREST LENDING Response of MONEY to Cholesky

-.05 .00 .05 .10 .15 .20

1 2 3 4

INVESTMENT INTEREST LENDING Response of INVESTMENT to Cholesky

-4 0 4 8 12

1 2 3 4

INVESTMENT INTEREST LENDING Response of INTEREST to Cholesky

-1 0 1 2 3

1 2 3 4

INVESTMENT INTEREST LENDING Response of LENDING to Cholesky

(21)

variables exhibit evidence of a feedback causal-effect (bi-directional). This is in accordance with earlier conclusion of a bi-directional relationship between CREDIT and GDPCAPITAL.

5.7 Variance Decomposition

The variance decomposition indicates the amount of information each variable contributes to the other variables in the autoregression. It determines how much of the forecast error variance of each of the variables can be explained by exogenous shocks to the other variables.

We employ a ten year forecasting time horizon and observed the relevance of the variables over time. However, only variance decomposition of GDPCAPITAL and CREDIT are shown.

Table 8: Variance Decomposition

Variance Decomposition

of GDPCAPITAL:

Period S.E. GDPCAPITAL CREDIT MONEY INVESTMENT INTEREST LENDING

1 0.047627 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 2 0.074733 87.52240 4.508408 0.002524 6.991313 0.891686 0.083668 3 0.095771 82.03949 5.266476 0.008529 11.37484 1.187251 0.123422 4 0.112977 78.70974 5.562416 0.014669 14.35464 1.223932 0.134609 5 0.127259 76.41152 5.681972 0.025818 16.52819 1.217460 0.135043 6 0.139148 74.77106 5.703086 0.044261 18.15003 1.202070 0.129498 7 0.149084 73.56202 5.669559 0.071720 19.38884 1.187059 0.120804 8 0.157424 72.64322 5.604464 0.109573 20.35645 1.175185 0.111103 9 0.164457 71.92347 5.521047 0.158835 21.12765 1.166873 0.102126 10 0.170418 71.34188 5.427438 0.220208 21.75336 1.161810 0.095312

Variance Decomposition

of CREDIT:

Period S.E. GDPCAPITAL CREDIT MONEY INVESTMENT INTEREST LENDING

1 3.130676 3.078353 96.92165 0.000000 0.000000 0.000000 0.000000 2 3.352511 5.306546 87.91570 1.130512 4.135801 1.478465 0.032971 3 3.447711 5.725337 84.30964 1.405246 7.016690 1.447556 0.095534 4 3.478246 5.805334 83.18828 1.684574 7.738618 1.422459 0.160730 5 3.492718 5.788616 82.70259 1.949214 7.910815 1.412446 0.236318 6 3.501648 5.759770 82.39855 2.196372 7.915958 1.408783 0.320567 7 3.509031 5.739417 82.12770 2.431100 7.884197 1.408042 0.409542 8 3.516181 5.727858 81.84919 2.656530 7.856795 1.408434 0.501193 9 3.523409 5.720941 81.55875 2.875021 7.841839 1.409140 0.594312 10 3.530715 5.714875 81.26174 3.088287 7.837104 1.409803 0.688196

Cholesky Ordering: GDPCAPITAL CREDIT MONEY INVESTMENT INTERESTN LENDING

(22)

Table 8 above gives the fraction of the forecast error variance for each variable that is attributed to its own innovation and to innovations in another variable. The own shocks of GDPCAPITAL constitute a significant source of variation in its forecast error in the time horizon, ranging from 100% to 71.3%. Ten years after, variation in GDPCAPITAL is accounted for by CREDIT (5.4%), MONEY (0.2%), INVESTMENT (21.7%), INTEREST(1.2%) and LENDING (0.1%) shock. It is clear that the predominant sources of variation in GDPCAPITAL are CREDIT and INVESTMENT. Similar explanations hold for the variations in growth in the other forecast periods.

6. Conclusion

This study gives empirical evidence on the nexus and the causality between domestic credit and economic growth in Nigeria using VAR and Granger Causality approach over the period 1970 - 2012. Firstly, the findings reveal that there is a bi-directional causality and positive relationship between domestic credit and the economic growth in Nigeria. That is, domestic credit does not only contribute positively to economic growth in Nigeria, but the impact is strong and statistically significant.

The fact that domestic credit does have positive significant relationship with economic growth reinforces the conclusion by earlier literature that an efficient financial system is one of the foundations for building sustained economic growth and an open, vibrant economic system..

Considering regulations, institutional constraints and other macro-economic factors militating against domestic credit in the economy, government should make the environment conducive and supportive so that performance is enhanced and good lending behaviour guaranteed. Banks need to start enforcing the most easily realizable policies as well as embrace good credit management. To further improve on their lending performance to the priority sectors of the economy, commercial banks should strategize on how to attract and retain more deposits. Closer consultation and cooperation between commercial banks and the regulatory authorities is necessary. This will enable the government to put into consideration the effect of regulatory measure on commercial banks at the stage of policy formulation.

(23)

With growing consumer spending power (not to mention a population of over 160 million), credit bureaus offer a unique opportunity to addressing the challenges of credit penetration and, by extension, financial sector deepening in Nigeria. To unlock this potential however, there needs to be in place an elaborate and robust country-wide identification system. A unique borrower’s identity, the lack of which has hampered the effectiveness of the few licensed credit bureaus currently operating in the country, remains the foundation upon which a deep and robust credit and financial system can be built. If this is implemented, the entire lending environment will be dramatically transformed in a few years. Many more people will be granted access to credit, credit granting will be a lot more robust and scientifically based, and a lot more organizations will have much larger credit portfolios than they used to.

(24)

REFERENCES

Banks resume big lending as Etisalat gets $650m. http://www.businessdayonline.com/NG FRIDAY, 11 MARCH 2011 00:00 JOHN OMACHONU & BLESSING ANARO

A. Akpansung and S. Babalola ( 2011) Banking Sector Credit and Economic Growth in Nigeria:

An Empirical Investigation CBN Journal of Applied Statistics Vol. 2 No.2 51

Dey, Malay K. & Flaherty, Susan (2005), “Stock Exchange Liquidity, Bank Credit, and Economic Growth”. Paper presented at the Max Fry Conference on Finance and Development, University of Birmingham, The Business School University House, Birmingham B15 2TT.

Mishra, P. K.; Das, K. B. & Pradhan, B. B. (2009). “Credit Market Development and Economic Growth in India”, Middle Eastern Finance and Economics, ISSN: 1450-2889, Issue

5 Muhsin, K. & Eric, J. P. (2000). “Financial Development and Economic Growth in Turkey:

Further Evidence on the Causality Issue” Centre for International, Financial and Economics Research Department of Economics Loughborough University

Cappiello, L., A. Kadareja, C.K. Sørensen and M. Protopapa, (2010).“Do Bank Loans and Credit Standards have an effect on Output? A Panel Approach for the Euro Area”, European Central Bank Working Paper Series, No 1150 / January

Ojo M. 1993. A Review and Appraisal of Nigeria’s Experience with Financial Sector Reform.

CBN Research Department, Occasional Paper No. 8, Lagos.

H. Wa (2005) Bank Credit and Economic Growth in Macao. Monetary Authority of Macao M. Iqbal, N. Ahmad and Z. Hussain Impact of Savings and Credit on Economic Growth in Pakistan Pakistan Journal of Social Sciences (PJSS) Vol. 32, No. 1 (2012), pp. 39-48

I. Radwan (2010) Financial Sector Liberalization and Challenges of Real Sector Financing in Nigeria: The World Bank Perspective Central Bank of Nigeria Economic and Financial Review Volume 48/4 December 2010

Abu-Bader, S., Abu-Qarn, A.S. (2006) Financial Development And Economic Growth Nexus:

Time Series Evidence From Middle Eastern And North African Countries, Monaster Center For Economic Research’s Discussion Paper No. 06-09, 34p.

Patrick, H.T. (1966) ‘Financial development and economic growth in underdeveloped countries”, Economic Development and Cultural Change, 14(1), 174-189. Economics, 22(1), 3-42.

Pradhan, R.P. (2010) Financial Deepening, Foreign Direct Investment And Economic Growth:

Are They Cointegrated?, International Journal Of Financial Research, 1(1):37-43

(25)

Levine, R. and S. Zervos.(1996). “Stock market development and long-run growth”, The World Economic Review, 110(2): 323-340

(26)

APPENDIX VAR Residual Normality Tests

Orthogonalization: Cholesky (Lutkepohl)

Null Hypothesis: residuals are multivariate normal Sample: 1970 2012

Component Skewness Chi-sq df Prob.

1 -0.971400 6.605323 1 0.0102

2 -4.000893 112.0500 1 0.0000

3 0.675115 3.190461 1 0.0741

4 -0.379173 1.006404 1 0.3158

5 -0.311197 0.677907 1 0.4103

6 0.040717 0.011605 1 0.9142

Joint 123.5417 6 0.0000

Component Kurtosis Chi-sq df Prob.

1 5.594377 11.77888 1 0.0006

2 23.96910 769.4808 1 0.0000

3 2.721385 0.135846 1 0.7124

4 3.287188 0.144335 1 0.7040

5 3.001747 5.34E-06 1 0.9982

6 4.416682 3.512228 1 0.0609

Joint 785.0521 6 0.0000

Component Jarque-Bera df Prob.

1 18.38421 2 0.0001

2 881.5308 2 0.0000

3 3.326307 2 0.1895

4 1.150739 2 0.5625

5 0.677912 2 0.7125

6 3.523833 2 0.1717

Joint 908.5938 12 0.0000

VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)

(27)

Date: 11/23/13 Time: 17:37 Sample: 1970 2012

Joint test:

Chi-sq df Prob.

280.3821 252 0.1058

OLS RESULT

Dependent Variable: GDPCAPITAL Method: Least Squares

Date: 11/23/13 Time: 18:08 Sample: 1970 2012

Variable Coefficient Std. Error t-Statistic Prob.

CREDIT -0.011655 0.004299 -2.711342 0.0101

MONEY 0.085547 0.009424 9.077733 0.0000

INVESTMENT 0.228372 0.044240 5.162092 0.0000

LENDING -0.006031 0.003298 -1.828560 0.0755

INTEREST -0.000104 0.001159 -0.089707 0.9290

C 4.173831 0.301764 13.83144 0.0000

(28)

Prob(F-statistic) 0.000000

OLS RESULT

Dependent Variable: GDPCAPITAL Method: Least Squares

Date: 11/23/13 Time: 18:41 Sample (adjusted): 1971 2012

Included observations: 42 after adjustments Convergence achieved after 11 iterations

Variable Coefficient Std. Error t-Statistic Prob.

C 4.207812 1.278289 3.291753 0.0023

CREDIT -0.004878 0.001741 -2.801029 0.0082

MONEY 0.088662 0.041599 2.131353 0.0402

INVESTMENT 0.048836 0.040401 1.208790 0.2348

LENDING -0.001191 0.002692 -0.442487 0.6609

INTEREST 0.000790 0.000497 1.590142 0.1208

AR(1) 0.942034 0.051080 18.44234 0.0000

Prob(F-statistic) 0.000000 Inverted AR Roots .94